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13 Cardozo L. Rev. 987 (1991 - 1992)
Distribution of Damages in Car Accidents through the Use of Neural Networks

handle is hein.journals/cdozo13 and id is 1009 raw text is: DISTRIBUTION OF DAMAGES IN CAR
ACCIDENTS THROUGH THE USE OF
NEURAL NETWORKS
Lothar Philipps*
ABSTRACT
After a traffic accident the damage has to be fairly divided
among the parties involved, and a ratio has to be determined.
There are many precedents for this, and judges have developed cat-
alogues suggesting ratios for common types of accidents.
The problem that every case is different, however, remains.
Many cases have familiar aspects, but also unfamiliar ones. Even if
a case is composed of several familiar aspects with established ra-
tios, the question remains as to how these are to be figured into one
ratio. The first thought would be to invent a mathematical
formula, but such formulae are rigid and speculative. The body of
law has grown organically and must not be forced into a sleek sys-
tem. The distant consequences of using a mathematical formula
cannot be foreseen; they might well be grossly unjust.
I suggest using a neural network instead. Precedents may be
fed into the network directly as learning patterns. This has the
advantage that court rulings can be transferred directly and not via
a formula. Future modifications in court rulings also can be
adopted by the network. As far as the effect of the learning pat-
terns on new cases is concerned, a relatively safe assumption is that
they will fit in harmoniously with the precedents. This is due to
the network's structure-a number of simple decisional units,
which are interconnected, tune their activity to each other, thus
achieving a state of equilibrium. When the conditions of such an
equilibrium are translated back into the terms of the case, the solu-
tion can hardly be totally unjust.
INTRODUCTION
A.
I would like to begin my discussion on the distribution of dam-
ages in car accidents by refering to a legal case of inheritance from
ancient Rome. This is not a far-fetched comparison; their children's
* Professor of Law, Institut ffir Rechtsphilosophie und Rechtsinformatik der Ludwig-
Maximilians-Universitat Munchen.

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